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• A package for producing graphics - gg = Grammar of Graphics

• Created by Hadley Wickham in 2005

• Belongs to “Tidyverse” family of packages

• “Make a ggplot” = Make a plot with the use of ggplot2 package

Based on the idea of:

plot objects are placed on top of each other with +

📉 

➕ 

📈 

• Pros: extremely powerful/flexible – allows combining multiple plot elements together, allows high customization of a look, many resources online

• Cons: ggplot2-specific “grammar of graphic” of constructing a plot

• ggplot2 gallery

To make graphics using ggplot2, our data needs to be in a tidy format

Tidy data:

1. Each variable forms a column.
2. Each observation forms a row.

Messy data:

• Column headers are values, not variable names.
• Multiple variables are stored in one column.
• Variables are stored in both rows and columns.

Each variable forms a column. Each observation forms a row.

Column headers are values, not variable names

Read more about tidy data and see other examples: Tidy Data tutorial by Hadley Wickham

It’s also helpful to have data in long format!!!

set.seed(3) 
var_1 <- seq(from = 1, to = 30)
var_2 <- rnorm(30)
my_data = tibble(var_1, var_2)
my_data

# A tibble: 30 × 2
   var_1   var_2
   <int>   <dbl>
 1     1 -0.962 
 2     2 -0.293 
 3     3  0.259 
 4     4 -1.15  
 5     5  0.196 
 6     6  0.0301
 7     7  0.0854
 8     8  1.12  
 9     9 -1.22  
10    10  1.27  
# … with 20 more rows

Will set up the plot - it will be empty!

• Aesthetic mapping (mapping= aes(x= , y =)) describes how variables in our data are mapped to elements of the plot

library(ggplot2) # don't forget to load ggplot2
# This is not code but shows the general format
ggplot({data_to plot},  mapping = aes(x = {var in data to plot},
                                      y = {var in data to plot}))

ggplot(my_data, mapping = aes(x = var_1, y = var_2))

There are many to choose from, to list just a few:

• geom_point() – points (we have seen)
• geom_line() – lines to connect observations
• geom_boxplot()
• geom_histogram()
• geom_bar()
• geom_col()
• geom_errorbar()
• geom_density()
• geom_tile() – blocks filled with color

ggplot({data_to plot},  mapping = aes(x = {var in data to plot},
                                      y = {var in data to plot})) +
  geom_{type of plot}</div>

ggplot(my_data, mapping = aes(x = var_1, y = var_2)) + 
  geom_point() 

Read as: add points to the plot (use data as provided by the aesthetic mapping)

Layer a plot on top of another plot with +

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point() + 
  geom_line()

You can change look of each layer separately.

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  geom_line(size = 0.8, color = "black", linetype = 2)

You can change the look of whole plot - specific elements, too - like changing font and font size - or even more fonts

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  geom_line(size = 0.8, color = "brown", linetype = 2) + 
  theme_bw(base_size = 20, base_family = "Comic Sans MS")

The labs() function can help you add or modify titles on your plot.

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  geom_line(size = 0.8, color = "brown", linetype = 2) + 
  labs(title = "My plot of var1 vs var2",
           x = "Variable 1",
           y = "Variable 2") + 
  theme(plot.title = element_text(hjust = 0.5))

xlim() and ylim() can specify the limits for each axis

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  geom_line(size = 0.8, color = "brown", linetype = 2) + 
  labs(title = "My plot of var1 vs var2") + 
  xlim(0,40)

scale_x_continuous() and scale_y_continuous() can change how the axis is plotted. Can use the breaks argument to specify how you want the axis ticks to be.

seq(from = 0, to = 30, by = 5)

[1]  0  5 10 15 20 25 30

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  geom_line(size = 0.8, color = "brown", linetype = 2) + 
  labs(title = "My plot of var1 vs var2") + 
  scale_x_continuous(breaks = seq(from = 0, to = 30, by = 5))

Lab document: http://jhudatascience.org//intro_to_r/Data_Visualization/lab/Data_Visualization_Lab.Rmd

The theme() function can help you modify various elements of your plot. Here we will adjust the horizontal placement of the plot title.

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  geom_line(size = 0.8, color = "brown", linetype = 2) + 
  labs(title = "My plot of var1 vs var2") + 
  theme(plot.title = element_text(hjust = 0.5, size = 20))

The theme() function always takes:

1. an object to change (use ?theme() to see - plot.title, axis.title, axis.ticks etc.)
2. the aspect you are changing about this: element_text(), element_line(), element_rect(), element_blank()
3. what you are changing:
   • text: size, color, fill, face, alpha, angle
   • position: “top”, “bottom”, “right”, “left”, “none”
   • rectangle: size, color, fill, linetype
   • line: size, color, linetype,

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  labs(title = "My plot of var1 vs var2", x = "Variable 1") +
  theme(plot.title = element_text(hjust = 0.5, size = 20),
        axis.title.x = element_text(size = 16))

Guide on how to: https://rpubs.com/mclaire19/ggplot2-custom-themes

First, we will generate some data frame for the purpose of demonstration.

• 20 different items (e.g. products in a store)
• of 2 different categories (e.g. pasta, rice)
• 100 price values collected over time for each item

# create 4 vectors: 2x character class and 2x numeric class
item_categ <- as.vector(sapply(1:20, function(i) rep(sample(c("pasta", "rice"), 1), 100)))
item_ID  <- rep(seq(from = 1, to = 20), each = 100)
item_ID <- paste0("ID_", item_ID)
observation_time  <- rep(seq(from = 1, to = 100), times = 20)
item_price <- as.vector(replicate(20, cumsum(rnorm(100))))
item_price <- item_price + abs(min(item_price)) + 1

# use 4 vectors to create data frame with 4 columns
df  <- data.frame(item_ID, item_categ, observation_time, item_price) 

head(df, 3)

  item_ID item_categ observation_time item_price
1    ID_1      pasta                1   21.68561
2    ID_1      pasta                2   21.37515
3    ID_1      pasta                3   23.07403

tail(df, 3)

     item_ID item_categ observation_time item_price
1998   ID_20      pasta               98   34.97370
1999   ID_20      pasta               99   35.34058
2000   ID_20      pasta              100   35.47118

str(df)

'data.frame':   2000 obs. of  4 variables:
 $ item_ID         : chr  "ID_1" "ID_1" "ID_1" "ID_1" ...
 $ item_categ      : chr  "pasta" "pasta" "pasta" "pasta" ...
 $ observation_time: int  1 2 3 4 5 6 7 8 9 10 ...
 $ item_price      : num  21.7 21.4 23.1 22.3 22.6 ...

You can use group element in a mapping to indicate that each item_ID will have a separate price line (more generally: a separate layer element)

ggplot(df, aes(x = observation_time, y = item_price, group = item_ID)) + 
  geom_line(size = 0.3) 

You can use color element in a mapping to indicate that each item_categ will have different color used.

Colors palette is selected by default (and can be modified). Legend position, legend title etc. have a default look (and can be modified).

ggplot(df, aes(x = observation_time, y = item_price, group = item_ID,
               color = item_categ)) + 
  geom_line(size = 0.3) 

Here, we use boxplot instead of lines.

Note how aesthetic mappings are defined now: aes(x = item_ID, y = item_price).

ggplot(df, aes(x = item_ID, y = item_price)) + 
  geom_boxplot() + 
  labs(x = "Item ID", y = "Item prices") + 
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

We use color element in mappings to indicate that each item_categ will have different color of boxplot box used.

We also use color = "Item\ncategory" to change name of legend.

ggplot(df, aes(x = item_ID, y = item_price, color = item_categ)) + 
  geom_boxplot() + 
  labs(x = "Item ID", y = "Item prices", color = "Item\ncategory") + 
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

We use fill element in mappings to indicate that each item_categ will have different color of boxplot filling used.

We also use fill = "Item\ncategory" to change name of legend.

ggplot(df, aes(x = item_ID, y = item_price, fill = item_categ)) + 
  geom_boxplot() + 
  labs(x = "Item ID", y = "Item prices", fill = "Item\ncategory") + 
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

Let’s make some tweaks to item ID (item_ID) ordering by creating a factor version with a certain order of factor levels

item_ID_levels <- 
  df %>% 
  group_by(item_ID) %>% 
  summarise(item_price_median = median(item_price)) %>%
  arrange(item_price_median) %>%
  pull(item_ID)

df <- 
  df %>%
  mutate(item_ID_factor = factor(item_ID, levels = item_ID_levels))

Same as 2 slides ago, but we replaced item_ID with item_ID_factor in mappings definition (aes()).

ggplot(df, aes(x = item_ID_factor, y = item_price, fill = item_categ)) + 
  geom_boxplot() + 
  labs(x = "Item ID", y = "Item prices", fill = "Item\ncategory") + 
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

We define data subset to keep only 4 (out of 20) item IDs. We use facet_grid(. ~ item_ID) to split the plot into panels where each product ID has a separate panel

df_subset <- df %>% 
  filter(item_ID %in% c("ID_1", "ID_2", "ID_3", "ID_4"))

ggplot(df_subset, aes(x = observation_time, y = item_price)) + 
  geom_line() + 
  labs(x = "Observation time", y = "Item prices") + 
  facet_grid(. ~ item_ID) 

We define data subset to keep only 4 (out of 20) item IDs. We use facet_grid(item_ID ~ .) to split the plot into panels where each product ID has a separate panel

ggplot(df_subset, aes(x = item_price)) + 
  geom_histogram(fill = "yellow", color = "brown", alpha = 0.5) + 
  labs(x = "Item prices", y = "Observations count") + 
  facet_grid(item_ID ~ .) 

A few options:

• RStudio > Plots > Export > Save as image / Save as PDF
• RStudio > Plots > Zoom > [right mouse click on the plot] > Save image as
• In the code

plot_FINAL <- 
  ggplot(df_subset, aes(x = item_price)) + 
  geom_histogram(fill = "yellow", color = "brown", alpha = 0.5) + 
  labs(x = "Item prices", y = "Observations count") + 
  facet_grid(item_ID ~ .)  

ggsave(filename = "very_important_plot.png",  # will save in working directory
       plot = plot_FINAL, 
       width = 6, height = 3.5)               # by default in inch

Lab document: http://jhudatascience.org//intro_to_r/Data_Visualization/lab/Data_Visualization_Lab.Rmd